{
 "metadata": {
  "name": "normalization-api"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "import ocrolib\n",
      "from scipy.ndimage import filters,interpolation\n",
      "from scipy import stats\n",
      "from ocrolib import lineest\n",
      "reload(lineest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<module 'ocrolib.lineest' from 'ocrolib/lineest.pyc'>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Generate Distorted Image"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(8,8)\n",
      "image = 1-ocrolib.read_image_gray(\"010036.bin.png\")\n",
      "image = interpolation.affine_transform(image,array([[1,0.03],[0,1]]),offset=(-30,0),output_shape=(100,700),order=0)\n",
      "imshow(image,cmap=cm.gray)\n",
      "h,w = image.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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2bdtSbm6uopzS++LgwYNERPTjjz+KCWSAcgxXwtfX967/33tB1Ki1hTt9+nQsXrwYdnZ/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\nYII=\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "MvNormalizer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(lineest)\n",
      "mv = lineest.MvNormalizer()\n",
      "mv.measure(image)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(mv.normalize(image,order=0),cmap=cm.gray)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.image.AxesImage at 0x607ae50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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TKPfo6enBzs5OkD8pKQm5ubkYNGgQoqOjoa2tjZKSEpHmZpcuXRAYGIjnz5+L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\nw2kC1ABwFyEcDofD4TQy/wv6nRjBa9BcEAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lineest Normalizer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(lineest)\n",
      "le = lineest.load_normalizer(\"en-mixed.lineest\")\n",
      "le"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<ocrolib.lineest.LineestNormalizer instance at 0x5ed8050>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "le.measure(image)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(le.normalize(image),cmap=cm.gray,interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.image.AxesImage at 0x66fbf50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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hGzCIEU+erIVVq1ZZbFNFRQVFRkayvKNGjaKcnBym7DZs2EDx8fH02muvEVHV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\n4XA4nL8J/w9wz1glNP03ZwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(le.normalize(image,order=0)[:,:40],cmap=cm.gray,interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.image.AxesImage at 0x6bf08d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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XX355SXbo2bNnUfONjY2ZZ7/xjW+ccOb1119v8+PucAEgAcEFgAQEFwASEFwASEBwASAB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\nBAQXABIQXABIQHABIAHBBYAE/h855u9xf8bS4QAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Saved MvNormalizer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(lineest)\n",
      "le = lineest.load_normalizer(\"../models/mvnorm-24.lnorm\")\n",
      "le.measure(image)\n",
      "imshow(le.normalize(image),cmap=cm.gray)\n",
      "le.target_height"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "24"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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DbBVlb2+PgIAAeHt74+rVqxg5cuQtF27KQ9yX+aarMuqKRqNBTU0NZDIZXFxc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\nQmCC\n"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Saved Gradnorm (NB: no deskewing)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(lineest)\n",
      "le = lineest.load_normalizer(\"../models/gradnorm-48.lnorm\")\n",
      "le.measure(image)\n",
      "imshow(le.normalize(image),cmap=cm.gray)\n",
      "le.target_height"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "48"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    }
   ],
   "metadata": {}
  }
 ]
}